> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# File operations

> Learn how to read, write, and mount files in W&B Sandboxes.

<Warning>
  W\&B Sandboxes is in private preview, available by invitation only. To request enrollment, contact [support](mailto:support@wandb.com) or your AISE.
</Warning>

Use file operations to share data between your local environment and a sandbox. You can read files from a sandbox, write files to it, or mount local files and directories into it.

For example, you can write a Python script to a sandbox, run it, and read the output file back to your local environment. You can also mount a directory of training data into a sandbox for a machine learning job.

<Tip>
  **Choose the right file access method**

  Mount files or directories when you want the sandbox to access local data without copying it.

  Read and write files when you want to transfer smaller files between your local environment and the sandbox, or when you want to save sandbox output locally.
</Tip>

## Write a file to the sandbox

{/* transfers a file into the sandbox. Use it to upload inputs your sandbox code needs (scripts, configs, data files). */}

Transfer a file from your local environment to the sandbox using the [`Sandbox.write_file()`](https://docs.coreweave.com/products/coreweave-sandbox/client/ref/core/sandbox#write_file) method.

```python theme={null}
from pathlib import Path
from wandb.sandbox import Sandbox

# Path of the local file you want to write to the sandbox
text_file = Path("hello.txt")

with Sandbox.run() as sandbox:
    # Write a file to the sandbox
    sandbox.write_file("hello.txt", text_file.read_bytes()).result()
```

See the `Sandbox` class reference documentation for a full list of parameters and options for [`Sandbox.write_file()`](https://docs.coreweave.com/products/coreweave-sandbox/client/ref/core/sandbox#write_file).

## Read a file from the sandbox

Save a file from the sandbox to your local environment using the [`Sandbox.read_file()`](https://docs.coreweave.com/products/coreweave-sandbox/client/ref/core/sandbox#read_file) method.

```python theme={null}
from pathlib import Path
from wandb.sandbox import Sandbox

with Sandbox.run() as sandbox:
    # Demo creates a file in the sandbox to read back
    sandbox.exec(["sh", "-c", "echo 'Hello, world.' > hello.txt"]).result()

    # Save the file on local machine (not sandbox)
    content = sandbox.read_file("hello.txt").result()  # b"Hello, world.\n"
    Path("hello.txt").write_bytes(content)
```

See the `Sandbox` class reference documentation for a full list of parameters and options for [`Sandbox.read_file()`](https://docs.coreweave.com/products/coreweave-sandbox/client/ref/core/sandbox#read_file).

## Mount a file or directory

Use mounted files to provide local files to the sandbox at creation time. Unlike `Sandbox.write_file()`, which transfers files to a running sandbox, mounted files are available as soon as the sandbox starts. Mounted files appear in the sandbox at the path you specify.

<Info>
  Mounted files are read-only in the sandbox. If you need to modify files in the sandbox, use `Sandbox.write_file()` instead.
</Info>

In the following code snippet, local files `train.py` and `requirements.txt` are mounted to the sandbox root directory. The sandbox installs dependencies from `requirements.txt` and then runs `train.py`.

```python theme={null}
from pathlib import Path
from wandb.sandbox import Sandbox, NetworkOptions

mounted_files = [
    {"mount_path": "requirements.txt", "file_content": Path("requirements.txt").read_bytes()},
    {"mount_path": "train.py", "file_content": Path("train.py").read_bytes()},
        ] 

print("Starting sandbox...")
with Sandbox.run(mounted_files=mounted_files) as sandbox:

    # The mounted files are available in the sandbox at the specified mount paths
    print("Installing dependencies...")
    result = sandbox.exec(["pip", "install", "-r", "requirements.txt"], check=True).result()
    print(result.stdout)

    print("Running script...")
    result = sandbox.exec(["python", "train.py"]).result()
    print(result.stdout)
```